Gosse Minnema


2022

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SocioFillmore: A Tool for Discovering Perspectives
Gosse Minnema | Sara Gemelli | Chiara Zanchi | Tommaso Caselli | Malvina Nissim
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event. Our tool, whose rationale we also support through a large collection of human judgements, is theoretically grounded on frame semantics and cognitive linguistics, and implemented using the LOME frame semantic parser. We describe SOCIOFILLMORE’s development and functionalities, show how non-NLP researchers can easily interact with the tool, and present some example case studies which are already incorporated in the system, together with the kind of analysis that can be visualised.

2021

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Breeding Fillmore’s Chickens and Hatching the Eggs: Recombining Frames and Roles in Frame-Semantic Parsing
Gosse Minnema | Malvina Nissim
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

Frame-semantic parsers traditionally predict predicates, frames, and semantic roles in a fixed order. This paper explores the ‘chicken-or-egg’ problem of interdependencies between these components theoretically and practically. We introduce a flexible BERT-based sequence labeling architecture that allows for predicting frames and roles independently from each other or combining them in several ways. Our results show that our setups can approximate more complex traditional models’ performance, while allowing for a clearer view of the interdependencies between the pipeline’s components, and of how frame and role prediction models make different use of BERT’s layers.

2020

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Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training
Christian Roest | Lukas Edman | Gosse Minnema | Kevin Kelly | Jennifer Spenader | Antonio Toral
Proceedings of the Fifth Conference on Machine Translation

Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English–Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.

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Towards Reference-Aware FrameNet Annotation
Levi Remijnse | Gosse Minnema
Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet

In this paper, we introduce the task of using FrameNet to link structured information about real-world events to the conceptual frames used in texts describing these events. We show that frames made relevant by the knowledge of the real-world event can be captured by complementing standard lexicon-driven FrameNet annotations with frame annotations derived through pragmatic inference. We propose a two-layered annotation scheme with a ‘strict’ FrameNet-compatible lexical layer and a ‘loose’ layer capturing frames that are inferred from referential data.

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Large-scale Cross-lingual Language Resources for Referencing and Framing
Piek Vossen | Filip Ilievski | Marten Postma | Antske Fokkens | Gosse Minnema | Levi Remijnse
Proceedings of the 12th Language Resources and Evaluation Conference

In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language. We provide our first results of data acquisition, together with the first data release. We introduce the notion of cross-lingual referential corpora. These corpora consist of texts that make reference to exactly the same incidents. The referential grounding allows us to analyze the framing of these incidents in different languages and across different texts. During the project, we will use the automatically generated data to study linguistic framing as a phenomenon, build framing resources such as lexicons and corpora. We expect to capture larger variation in framing compared to traditional approaches for building such resources. Our first data release, which contains structured data about a large number of incidents and reference texts, can be found at http://dutchframenet.nl/data-releases/.

2019

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Toward Dialogue Modeling: A Semantic Annotation Scheme for Questions and Answers
María Andrea Cruz Blandón | Gosse Minnema | Aria Nourbakhsh | Maria Boritchev | Maxime Amblard
Proceedings of the 13th Linguistic Annotation Workshop

The present study proposes an annotation scheme for classifying the content and discourse contribution of question-answer pairs. We propose detailed guidelines for using the scheme and apply them to dialogues in English, Spanish, and Dutch. Finally, we report on initial machine learning experiments for automatic annotation.

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From Brain Space to Distributional Space: The Perilous Journeys of fMRI Decoding
Gosse Minnema | Aurélie Herbelot
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Recent work in cognitive neuroscience has introduced models for predicting distributional word meaning representations from brain imaging data. Such models have great potential, but the quality of their predictions has not yet been thoroughly evaluated from a computational linguistics point of view. Due to the limited size of available brain imaging datasets, standard quality metrics (e.g. similarity judgments and analogies) cannot be used. Instead, we investigate the use of several alternative measures for evaluating the predicted distributional space against a corpus-derived distributional space. We show that a state-of-the-art decoder, while performing impressively on metrics that are commonly used in cognitive neuroscience, performs unexpectedly poorly on our metrics. To address this, we propose strategies for improving the model’s performance. Despite returning promising results, our experiments also demonstrate that much work remains to be done before distributional representations can reliably be predicted from brain data.